Information processing apparatus, information processing method, non-transitory storage medium, and information processing system

ABSTRACT

One embodiment of the invention provides an apparatus preventing degradation of accuracy of a search result while reducing a total required period of a series of processing in a case where input parameters for a series of processing including a plurality of processing with different required periods are searched for. The apparatus includes a calculator and a generator. The calculator calculates evaluation values for output parameters of a series of processing including first processing and second processing. The first processing uses a first input parameter. The second processing use a second input parameter. The generator regenerates first and second input parameters corresponding to one time of a series of processing based on first and second input parameters corresponding to selected output parameters. The number of input parameters for shorter one of the first and the second processing is larger than the number of input parameters for the other.

CROSS-REFERENCE TO RELATED APPLICATION (S)

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2021-015257, filed Feb. 2, 2021; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an informationprocessing apparatus, an information processing method, a non-transitorystorage medium, and an information processing system.

BACKGROUND

As a method for efficiently searching for a favorable input parameterwhich makes an objective function a maximum or a minimum, a Bayesianoptimization method is known. In the Bayesian optimization method, anacquisition function is created on the basis of a result of a simulationor the like, and an input parameter which makes the current acquisitionfunction a maximum is determined as the next input parameter. It isknown that use of the Bayesian optimization method in a simulation whichtakes time can make knowledge of technical experts non-essential, andthus, a period for efficiently searching for an input parameter can bereduced.

However, there can be a case where it is difficult to shorten the periodeven if the Bayesian optimization is used. For example, there is a casewhere an input parameter which is to be used in each sub-simulation of aseries of simulations which sequentially executes a plurality ofsub-simulations is searched for. In this case, if Bayesian optimizationis performed on the series of simulations, it takes an enormous periodof time for calculation, and a search period cannot be shortened somuch.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an information processing systemaccording to an embodiment of the present invention;

FIG. 2 is a diagram illustrating flow of a simulation in related art;

FIG. 3 is a diagram illustrating flow of a simulation in the presentembodiment (diagram for one trial);

FIG. 4 is a diagram illustrating an example of a 1st first inputparameter “X1”;

FIG. 5 is a diagram illustrating an example of a parameter “U”;

FIG. 6 is a diagram illustrating an example of a parameter “Z”;

FIG. 7 is a diagram illustrating an example of a parameter “Y”;

FIG. 8 is a diagram illustrating an example of an evaluation value to becalculated;

FIG. 9 is a diagram explaining selection;

FIG. 10 is a diagram illustrating an example of data regarding aselected parameter to be stored in a storage;

FIG. 11 is a diagram illustrating an example of output;

FIG. 12 is a schematic flowchart of a series of processing of theinformation processing system; and

FIG. 13 is a block diagram illustrating an example of a hardwareconfiguration in one embodiment of the present invention.

DETAILED DESCRIPTION

One embodiment of the present invention provides an apparatus, or thelike, which prevents degradation in accuracy of a search result whilereducing a total required period of a series of processing in a casewhere input parameters for a series of processing including a pluralityof processing with different required periods are searched for.

An apparatus which is one embodiment of the present invention includes acalculator and a generator. The calculator calculates evaluation valuesfor output parameters of a series of processing including firstprocessing and second processing, the first processing using a firstinput parameter and the second processing using a second inputparameter. The generator regenerates first and second input parameterscorresponding to one time of a series of processing on the basis offirst and the second input parameters corresponding to selected outputparameters. Further, the number of input parameters generated for one ofthe first processing and the second processing which has a shorterprocessing period is larger than the number of input parametersgenerated for the other.

An embodiment will be explained in detail below with reference to theaccompanying drawings. The present invention is not limited to theembodiment.

One Embodiment of the Present Invention

FIG. 1 is a block diagram illustrating an information processing systemincluding a parameter provision apparatus (information processingapparatus) according to one embodiment of the present invention. Aninformation processing system 1 according to the present embodimentincludes a parameter provision apparatus (information processingapparatus) 11, a first processing apparatus 12A, a second processingapparatus 12B, and a management apparatus 13. The parameter provisionapparatus 11 includes a parameter generator 111, an evaluation valuecalculator 112, a selector 113, a storage 114, a determiner 115 and aninput/output device 116.

Note that a configuration of the present embodiment is an example, andother apparatuses and components may be included. Further, respectiveapparatuses and components may be subdivided or may be aggregated. Inthe information processing system, respective processing are oftenshared by a plurality of apparatuses to disperse processing load,maintain availability, or the like. For example, while the parametergenerator 111 generates parameters to be used by the first processingapparatus 12A and the second processing apparatus 12B, the parametergenerator 111 may be divided into a first parameter generator whichgenerates a parameter to be used by the first processing apparatus 12Aand a second parameter generator which generates a parameter to be usedby the second processing apparatus 12B. Further, the input/output device116 may be divided into an input device and an output device. Further,the information processing apparatus may be divided into an apparatusincluding the parameter generator 111, and an apparatus including othercomponents. Further, the first processing apparatus 12A and the secondprocessing apparatus 12B do not have to be different apparatuses and maybe one apparatus.

The information processing system 1 in the present embodiment searchesfor a favorable value of an input parameter required for processing suchas a simulation. For example, a simulation based on the input parameteris executed, an output parameter which is an execution result isevaluated, and a value of the input parameter is redetermined such thatthe evaluation becomes higher. Note that a value of an input parametereventually having the highest evaluation may be selected as an optimalvalue. Note that a value which is more appropriate than the valueselected as the optimal value may actually exist. Further, in thefollowing description, determination and generation of a parameter meandetermination of a value of the parameter, not determination of a typeof the parameter to be used for a simulation or the like.

Note that processing to which an input parameter is provided may beprocessing which receives an input parameter and returning an outputparameter and may be an experiment which outputs a result correspondingto a certain condition if the processing is executed under thecondition. Note that the experiment may be either a chemical experimentor a physical experiment.

Note that the number of input parameters and the number of outputparameters are not particularly limited. Thus, in the followingdescription, the input parameter and the output parameter will be dealtwith as vectors including one or more elements. Further, contentindicated by the input parameter and content indicated by the outputparameter are not particularly limited. For example, in a case where aseries of processing is an experiment, values of a condition upon theexperiment may be set as values of the input parameter. Further, valuesof the output parameter may be measurement values obtained by variouskinds of sensors used in the experiment or physical characteristicvalues or measurement values output from an experiment result. Values ofrespective elements of respective parameters may be, for example,continuous values, discrete values or category variables.

Further, it is assumed that a simulation, or the like, to be executed inthe information processing system 1 is one overall simulation which isconstituted with at least two simulations. Thus, in the followingdescription, the entire simulation, the entire experiment, or the likewill be described as a “series of processing”, and processing such aspartial simulations and partial experiments which constitute a series ofprocessing will be described as “sub-processing”. Note that thesub-processing may further include a plurality of partial processing.

In the present embodiment, a series of processing includes firstsub-processing and second sub-processing, and the parameter provisionapparatus 11 provides input parameters to the first sub-processing andthe second sub-processing. The first sub-processing is executed on thebasis of an input parameter for the first sub-processing and outputs anoutput parameter. The second sub-processing is executed on the basis ofan input parameter for the second sub-processing and the outputparameter of the first sub-processing. Thus, the second sub-processingdepends on the output parameter of the first sub-processing.

Further, it is assumed that periods required for executing thesub-processing are different from each other. In the present embodiment,the number of times of execution per one time of a series of processingis made smaller for sub-processing which requires a relatively longerperiod among respective sub-processing, and the number of times ofexecution per one time of a series of processing is made larger forsub-processing which requires a relatively shorter period among therespective sub-processing. This shortens a total period for executing aseries of processing to be executed by the information processingsystem.

Note that the sub-processing for which the number of times of executionis made smaller will be also described as sub-processing which is to beexecuted less times. The sub-processing for which the number of times ofexecution is made larger will be also described as sub-processing whichis to be executed more times. In the present embodiment, the firstsub-processing is sub-processing which is to be executed less times, andthe second sub-processing is sub-processing which is to be executed moretimes.

FIG. 2 and FIG. 3 are diagrams explaining a difference in flow of aseries of processing between related art and the present embodiment.FIG. 2 is a diagram illustrating flow of a series of processing in therelated art. An alphabetic character “X” indicated in FIG. 2 indicatesan input parameter for the first sub-processing, which is generated bythe parameter provision apparatus 11. The parameter “X” will be alsodescribed as a first input parameter. An alphabetic character “U” inFIG. 2 indicates an output parameter of the first sub-processing and isalso an input parameter for the second sub-processing. The parameter “U”will be also described as a first output parameter. An alphabeticcharacter “Z” in FIG. 2 indicates an input parameter for the secondsub-processing, which is generated by the parameter provision apparatus11. The parameter “Z” will be also described as a second inputparameter. An alphabetic character “Y” in FIG. 2 indicates an outputparameter of the second sub-processing. The parameter “Y” will be alsodescribed as a second output parameter. Note that in the example in FIG.2 and FIG. 3, the first input parameter “X” and the second inputparameter “Z” can be also regarded as input parameters of a series ofprocessing, and the second output parameter “Y” can be also regarded asan output parameter of a series of processing.

Further, while a number 1 is provided to each parameter in FIG. 2, thisindicates a 1st parameter, and a p-th (where p is an integer equal to orgreater than 1) parameter will be described as, for example, “Xp”, “Up”,or the like. The 1st first input parameter “X1” for the firstsub-processing is transmitted from the parameter provision apparatus 11to the first processing apparatus 12A, and the first processingapparatus 12A performs the first sub-processing on the basis of theinput parameter “X1”. A parameter “U1” is output from the firstprocessing apparatus 12A through the first sub-processing based on theinput parameter “X1”. Further, an input parameter “Z1” of the first timefor the second processing apparatus 12B is transmitted from theparameter provision apparatus 11 to the second processing apparatus 12B,and the second processing apparatus 12B performs the secondsub-processing on the basis of the parameters “Z1” and “U1”. An outputparameter “Y1” is output from the second processing apparatus 12Bthrough the second sub-processing based on the parameter “Z1” and theparameter “U”. The output parameter “Y1” is transmitted to the parameterprovision apparatus 11, and the parameter provision apparatus 11determines input parameters “X2” and “Z2” of the second time on thebasis of the output parameter “Y1”. In this manner, the input parameteris changed every time a series of processing is performed once. Afavorable value of the input parameter is searched for by iterating thisprocessing.

To secure a certain level of accuracy of a search result, a series ofprocessing is required to be executed a certain number of times. Forexample, the number of times of execution of a series of processing canbe determined at 100 times. Further, for example, it is assumed that anexecution period of the first sub-processing by the first processingapparatus 12A is 100 seconds, and an execution period of the secondsub-processing by the second processing apparatus 12B is one second. Inthis case, it takes 101 seconds to execute a series of processing once(100 seconds for the first sub-processing+one second for the secondsub-processing), and thus, a total required period of a series ofprocessing becomes 10100 seconds (101 seconds per one time×100 times).

Meanwhile, in the present embodiment, the number of times of executionof a series of processing is reduced. While the number of times ofexecution of a series of processing is 100 times in a case of relatedart described above, in the present embodiment, the number of times ofexecution of a series of processing is reduced, and is, for example, setat 10 times. While accuracy of search degrades with the number of timesof execution of 10 times, the second sub-processing which ends in ashort period of time is executed a plurality of times in a series ofprocessing to maintain accuracy of search as high as possible. Forexample, the second sub-processing is executed 100 times per one time ofa series of processing. In this case, it takes a total of 200 secondsincluding 100 seconds for the execution period of the firstsub-processing and 100 seconds for a total execution period of thesecond sub-processing (one second×100 times) in one time of a series ofprocessing. While it takes a longer period to execute a series ofprocessing once, the number of times of execution is reduced to 10times, and thus, it takes a total of 2000 seconds (200×10) to execute aseries of processing, which is reduced compared to the related art.

FIG. 3 is a diagram illustrating flow of a series of processing of thepresent embodiment. The first sub-processing by the first processingapparatus 12A is the same as the processing illustrated in FIG. 2.Meanwhile, the parameter provision apparatus 11 transmits 100 parametersfrom “Z1” to “Z100” (expressed as “{Z1, Z2, Z3, . . . , Z100}” in FIG.3) to the second processing apparatus 12B as input parameters of thefirst time for the second sub-processing. The second processingapparatus 12B executes the second sub-processing for each of 100parameters from “Z1” to “Z100”. In other words, the second processingapparatus 12B executes the second sub-processing on the basis ofparameters “U1” and “Z1”, then, executes the second sub-processing onthe basis of the parameters “U1” and “Z2”, and then, executes the secondsub-processing on the basis of the parameters “U1” and “Z3”. The secondsub-processing corresponding to 100 parameters from “Z1” to “Z100” isexecuted in this manner. Then, output parameters “Y1” to “Y100” of thesecond sub-processing are transmitted to the parameter provisionapparatus 11. Note that FIG. 3 indicates parameters “U” and “Z” used forgeneration of a parameter “Yp” within parentheses after the parameter“Yp” (“p” is an integer equal to or greater than 1). For example, “Y2(U1, Z2)” indicates that the parameter “Y2” is generated on the basis ofthe parameters “U1” and “Z2”.

The parameter provision apparatus 11 determines an input parameter “U2”and the input parameters “Z101” to “Z200” to be used in a series ofprocessing of the second time on the basis of the output parameters “Y1”to “Y100”. In this manner, at least sub-processing which takes a shorterperiod is executed a plurality of times instead of each kind ofsub-processing being executed once per one time of a series ofprocessing. This minimizes degradation of performance of search even ifthe number of times of execution of a simulation is reduced. Thus,between the first input parameters and the second input parameters forone time of a series of processing, the number of input parameters usedfor processing which takes a shorter execution period, between the firstsub-processing and the second sub-processing, is made larger. That is,when the processing period of the first sub processing is shorter thanthat of the second sub processing, the number of input parameters forthe first sub processing is made larger than that of the second subprocessing. When the processing period of the second sub processing isshorter than that of the first sub processing, the number of inputparameters for the second sub processing is made larger than that of thefirst sub processing.

Note that for explanatory convenience, while one first parameter “X” isgenerated in the example in FIG. 3, the parameter provision apparatus 11may generate a plurality of first parameters “X” at one time. Forexample, the parameter provision apparatus 11 may generate parameters“X1”, “X2” and “X3” and transmit the parameters to the first processingapparatus 12A. In this case, the first processing apparatus 12A executesthe first sub-processing for each of the parameters “X1”, “X2” and “X3”and outputs first output parameters “U1”, “U2” and “U3”. Then, thesecond processing apparatus 12B executes the second sub-processing foreach of the first output parameters “U1”, “U2” and “U3”. Note that theparameter provision apparatus 11 may generate the second parameter “Z”for each of the first output parameters “U1”, “U2” and “U3”. In a casewhere 100 second input parameters “Z” are generated in one time of asimulation as described above, second input parameters “Z1” to “Z100”may be generated for the first output parameter “U1”, second inputparameters “Z101” to “Z200” may be generated for the first outputparameter “U2”, and second input parameters “Z201” to “Z300” may begenerated for the first output parameter “U3”. Alternatively, the secondinput parameters “Z1” to “Z100” may be respectively combined with thefirst output parameters “U1”, “U2” and “U3”. Note that as describedabove, the number of the first input parameters per one time of a seriesof processing is made smaller than the number of the second inputparameters per one time of a series of processing.

How the parameters are combined only requires to be designated inadvance and stored in the storage 114. Further, in a case whereinformation regarding how the parameters are combined is received fromthe management apparatus 13, information in the storage 114 may beupdated.

Note that as described above, the parameters “X”, “U”, “Z” and “Y”include one or more elements. FIG. 4 is a diagram illustrating anexample of the 1st first input parameter “X1”. FIG. 4 indicates valuesfor each element of the parameter “X”. A k-th element of the parameter“X1” is expressed as “x_(1_k)”, and expressed using a lower-casealphabetic character and a subscript number. In the example in FIG. 4,the input parameter “X1” includes 50 elements of “x_(1_1), x_(1_2), . .. , x_(1_50)”.

FIG. 5 is a diagram illustrating an example of the parameter “U”. FIG. 5indicates values for each element of the 1st first output parameter“U1”. A k-th element of the parameter “U1” is expressed as “u_(1_k)” andis expressed using a lower-case alphabetical character and a subscriptnumber in a similar manner to the parameter “X1”. In the example in FIG.5, the input parameter “U1” includes 10 elements of “u_(1_1), u_(1_2), .. . , u_(1_10)”.

FIG. 6 is a diagram illustrating an example of the parameter “Z”. FIG. 6indicates values for each element of 1st to 100th second inputparameters “Z1” to “Z100”. In the example in FIG. 6, the parameter “Z”includes two elements. For example, a value of the 1st element “Z₁₋₁” ofthe parameter “Z1” is 0.5, and a 2nd element “Z₁₀₀₋₂” of the parameter“Z100” is 1.2.

FIG. 7 is a diagram illustrating an example of the parameter “Y”. FIG. 7indicates values for each element of 1st to 100th second outputparameters “Y1” to “Y100”. In the example in FIG. 7, the parameter “Y”includes six elements. For example, the 1st element “Y₁₋₁” of theparameter “Y1” is 1.8, and the 6th element “Y₁₀₀₋₆” of the parameter“Y100” is 2.4.

Note that it is assumed in the present embodiment that the inputparameters generated by the parameter provision apparatus 11 aretransmitted to the respective processing apparatuses 12, and therespective processing apparatuses 12 execute sub-processing using theinput parameters. However, the input parameters may be transmitted tothe management apparatus 13. A user who recognizes the input parametersvia the management apparatus 13 may set the input parameters at therespective processing apparatuses 12. In other words, there can be acase where the parameter provision apparatus 11 do not automaticallycoordinate with the respective processing apparatuses 12.

Further, all the input parameters generated by the parameter provisionapparatus 11 do not have to be used for the sub-processing. For example,in a case where it is determined that a total execution period of thesub-processing exceeds an upper limit value if all the received inputparameters are used, the respective processing apparatuses 12 may selectinput parameters to be actually used among the received input parametersand may perform the sub-processing using the selected input parameters.

Note that a series of processing may include three or moresub-processing. In this case, the third and subsequent sub-processinguses output parameters of preceding sub-processing as input parametersin a similar manner to the second sub-processing. Thus, it can be saidthat a series of processing is constituted with a plurality ofsub-processing which is to be executed in series. Further, outputparameters of the subsequent sub-processing depend on output of thepreceding sub-processing. Note that the third and subsequentsub-processing may receive the input parameters from the parameterprovision apparatus 11 or may use only output parameters of thepreceding sub-processing as the input parameters in a similar manner tothe second sub-processing. Note that in a case where a series ofprocessing includes three or more sub-processing, the lastsub-processing is preferably sub-processing which is to be executed moretimes, and it is possible to prevent a period required for executing onetime of a series of processing from becoming enormous by reducing inputparameters to be provided to sub-processing other than the lastsub-processing and increasing input parameters to be provided to thelast sub-processing.

Details of the processing of the parameter provision apparatus 11 willbe described along with components of the parameter provision apparatus11.

The parameter generator 111 generates input parameters for respectivesub-processing. Note that the input parameters corresponding to one timeof a series of processing are generated for each time. In this event,more input parameters are generated for sub-processing which is to beexecuted more times than for sub-processing which is to be executed lesstimes. Note that the number of generated input parameters may bedetermined in advance. Note that as described above, the second andsubsequent sub-processing uses at least output parameters of thepreceding sub-processing, and thus, the parameter generator 111 does notgenerate all input parameters of the respective processing apparatuses12.

The parameter generator 111 generates new input parameters on the basisof input parameters which are determined as favorable on the basis of aresult of the previous series of processing. Whether or not the inputparameters are favorable is determined by the selector 113 which will bedescribed later.

The input parameters may be generated using a method in related art. Forexample, Bayesian optimization (BO) or evolution strategy such ascovariance matrix adaptation evolution strategy (CMA-ES) and geneticalgorithm (GA) may be used. For example, parameters included in afunction which indicates relationship between the input parametersselected so far and evaluation values may be updated on the basis ofGaussian process, or the like, and values of input parameters which makeoutput of the updated function a maximum may be set as values of inputparameters for the next time.

For example, in a case where Bayesian optimization is used, posteriorprobabilities of evaluation values for the input parameters areestimated in accordance with a Bayesian rule using combinations of theinput parameters and evaluation values for output parameters generatedby the input parameters. The posterior probability may be expressed as arelational expression between the input parameters and an average valueand dispersion of the evaluation values. The relational expression ofthe average value and dispersion may be estimated using a method inrelated art such as Gaussian process regression and random forestregression.

For example, in a case where Gaussian process regression is used, ani-th (where “i” is an integer equal to or greater than 1) parameteramong n evaluated target parameters is set as “a_(i)”, a j-th (“j” is aninteger equal to or greater than 1) parameter is set as “a_(j)”, and anevaluation value corresponding to the element a_(i) is set as “b_(i)”.Further, it is assumed that an average value “m_(i)” of the evaluationvalue “b_(i)” is expressed as a function “μ_(o)(a_(i))”, and covariance“K_(i,j)” of “a_(i)” and “a_(j)” is expressed as a function “k(a_(i),a_(j))”. In other words, it is assumed that “m_(i)=μ_(o)(a_(i))” and“k_(i,j)=k(a_(i), a_(j))” hold. Note that “μ_(o)(a_(i))” is an arbitraryfunction, and “k(a_(i), a_(i))” is an arbitrary kernel function. Thekernel function may use a method in related art, for example, squaredexponential kernel, Matern kernel, linear kernel, Gaussian kernel, orthe like. In this event, a relational expression “μ_(n)(a)” of aparameter “a” and an average value of evaluation values “b” for theparameter “a” is expressed as the following expression.

μ_(n)(a)=μ_(o)(a)+k(a)^(T)(K+σ ² ·l)⁻¹(B−m)  [Expression 1]

Note that a vector “m” is a vector having an i-th element of “m_(i)”,and a matrix “K” is a matrix having i, j elements of “K_(i,j)”, a vector“k(a)” is a vector having an i-th element of “k_(i)(a)”, and“k_(i)(a)=k(a, a_(i))”, and “σ²” is an arbitrary constant. Further, arelational expression “σ_(n) ²(a)” of dispersion of the evaluationvalues “b” for the parameter “a” is expressed as the followingexpression.

σ_(a) ²(a)=k(a,a)−k(a)^(T)(K+σ ² l)⁻¹ k(a)  [Expression 2]

The estimated posterior probabilities are used as an acquisitionfunction. The acquisition function may use a method in related art suchas probability of improvement (PI), expected improvement (EI), expectedhypervolume improvement (EHVI), upper confidence bound (UCB), Thompsonsampling (TS), entropy search (ES) and mutual information (MI). Forexample, in a case where PI is used as a method of the acquisitionfunction, an acquisition function “α_(n)(A)” for a parameter “A” can becalculated using the following expression using an arbitrary constant“τ_(n)”.

α_(n)(A)=∫_(τ) _(n) ^(∞) p(B|A)dB  [Expression 3]

The parameter generator 111 regenerates the input parameters so as tomake the acquisition function as described above a maximum. Theacquisition function may be maximized using a method in related art. Forexample, full search, random search, grid search, a gradient method,limited memory BFGS method (L-BFGS), Dividing RECTanble, CMA-ES,multi-start local search, or the like, may be used.

Note that there can be a case where a plurality of first inputparameters are generated per one time of a series of processing, and thefirst sub-processing is executed for each of the first input parameters,in which case, local penalization may be performed. In other words, forsome first input parameters, parameters may be regenerated so as to makethe acquisition function a maximum, and for the remaining first inputparameters, parameters may be regenerated by degrading a value of theacquisition function in preceding parameters.

Note that there can be a case where the parameter generator 111generates random numbers on the basis of normal distribution, uniformdistribution, or the like, and the generated random numbers are set asthe input parameters. For example, the generated random numbers are setas the input parameters in a case where input parameters are generatedfor the first time, or in a case where the generated input parametersare not favorable and the input parameters are generated withoutdepending on the previous input parameters. Further, pseudo randomnumbers may be generated using a Sobol sequence, latin hypercubesampling, or the like, and may be set as the input parameters. Further,values of the input parameters may be adjusted using evolution strategy.Still further, values of the parameters may be adjusted on the basis ofa predetermined expression stored in the storage 114 in advance.Further, part of the input parameters to be generated may be randomlygenerated. Input parameters for few trials may be regenerated to havefavorable values, and input parameters for many trials may be randomlygenerated.

The parameter generator 111 transmits the generated parameters to therespective processing apparatuses 12 which execute the sub-processing.In the present embodiment, the generated parameter “X” is transmitted tothe first processing apparatus 12A, and the generated parameter “Z” istransmitted to the second processing apparatus 12B. Note that in a casewhere the first output parameter “U” is not directly transmitted fromthe first processing apparatus 12A to the second processing apparatus12B, the parameter generator 111 may transmit the first output parameter“U” to the second processing apparatus 12B.

Further, as described above, there can be a case where the firstprocessing apparatus 12A performs the first sub-processing a pluralityof times on the basis of one parameter “X” and outputs parameters “U”which are different from each other. For example, there can be a casewhere the first processing apparatus 12A performs the firstsub-processing three times on the basis of the parameter “X1” andoutputs the parameters “U1”, “U2” and “U3”. In this case, the parametergenerator 111 which has received the parameters “U1” to “U3” from thefirst processing apparatus 12A may transmit part of the parameters tothe second processing apparatus 12B instead of transmitting all theparameters “U1” to “U3”. For example, in a case where the number ofcombinations of the parameters “U” and “Z” exceeds a predetermined upperlimit value, it takes too much time, and thus, the parameters “U” may benarrowed down in accordance with an execution period of the secondsub-processing. A method for narrowing down the parameters may bedetermined as appropriate. The parameters to be narrowed down may berandomly selected or may be determined on the basis of values ofrespective elements.

Note that a range of values of parameters to be generated, a method forgenerating parameters, or the like, may be designated via the managementapparatus 13. The designation only requires to be stored in the storage114 and loaded upon generation of parameters. Further, the range, themethod, or the like, may be designated for each of the elements ofparameters or may be designated for all the elements of the parameters.

Further, the parameter generator 111 may determine settings regardingthe sub-processing as well as generate parameters. For example, theparameter generator 111 may determine the number of times of executionof respective sub-processing in one time of a series of processing, thatis, the number of input parameters in one time of a series of processing(the number of parameters “Z” in the present embodiment) on the basis ofthe execution period of the sub-processing of the respective processingapparatuses 12. For example, the number of input parameters for therespective sub-processing may be determined on the basis of an executionperiod “t₁” per one time of the first sub-processing, and an executionperiod “t₂” per one time of the second sub-processing. Morespecifically, a ratio of the execution period per one time of the firstsub-processing to the execution period per one time of the secondsub-processing may be set as the number of second input parameters “Z”.In other words, the second input parameters “Z” corresponding to thenumber of the ratio “t₁/t₂” may be generated for one first inputparameter “X”. Further, the number of parameters “Z” for which athreshold is set so as not to exceed this ratio may be determined asappropriate. In such a case, the parameter generator 111 can be regardedas a manager which manages settings of the sub-processing.

The evaluation value calculator 112 calculates evaluation values forrespective output parameters on the basis of an evaluation functionwhich uses output parameters as arguments. The evaluation function maybe determined as appropriate. For example, the evaluation function maybe linear mapping, non-linear mapping such as a sigmoid function, or acombination of a plurality of methods. For example, in a case whereideal output parameters are known in advance, a difference betweenactual output parameters and the ideal output parameters may be set asthe evaluation value. Further, a calculated value such as a sum, aproduct and a combination thereof of respective elements of the outputparameters may be set as the evaluation value.

Further, the evaluation value calculator 112 may calculate oneevaluation value for each parameter “Y” or may calculate a plurality ofevaluation values. For example, a plurality of evaluation functions maybe prepared, and evaluation values may be calculated respectively forthe plurality of evaluation functions. Further, evaluation values forrespective elements of the parameter “Y” may be calculated. A pluralityof evaluation values may be calculated for respective elements of theparameter “Y” using a plurality of evaluation functions.

FIG. 8 is a diagram illustrating an example of an evaluation value to becalculated. In the example in FIG. 8, evaluation values for theparameter “Y” are calculated respectively for evaluation functions usingtwo evaluation functions.

The selector 113 selects a favorable combination from combinations ofinput parameters corresponding to respective evaluation values, that is,combinations of input parameters used in the previous series ofprocessing on the basis of the evaluation values from the evaluationvalue calculator 112. In the present embodiment, a favorable combinationof the first input parameter “X” and the second input parameter “Z” isselected. Whether or not the combination is a favorable combination onlyrequires to be determined on the basis of whether the evaluation valuesatisfies a condition for selection (selection condition). In otherwords, the parameter “Y” corresponding to the evaluation value whichsatisfies the selection condition is selected, and the parameters “X”and “Z” which have involved in generation of the selected parameter “Y”are selected.

The selection condition may be determined as appropriate. For example,in a case where evaluation is higher as the evaluation value is smaller,the parameter “Y” corresponding to a minimum evaluation value may beselected. In a case where evaluation is higher as the evaluation valueis greater, the parameter “Y” corresponding to a maximum evaluationvalue may be selected. Further, in a case where a plurality ofevaluation values are calculated for each parameter “Y”, for example, aplurality of parameters “Y” are compared two by two, a parameter havingmore excellent evaluation values remains, and a parameter “Y” which isremaining at the end may be determined as the most favorable result.Alternatively, which of the two parameters “Y” is superior is determinedon the basis of a sum of differences of respective evaluation values,and a parameter “Y” remaining at the end may be determined as an optimumparameter.

Further, in a case where there are a plurality of evaluation valueswhich satisfy the selection condition, all the parameters “Y”corresponding to the evaluation values may be selected. For example, inthe selection condition which selects parameters “Y” for whichevaluation values are equal to or greater than a threshold, a pluralityof parameters “Y” for which evaluation values are equal to or greaterthan the threshold may be selected. Further, for example, only theparameters “Y” under control of other parameters “Y” may be excludedfrom selection. In other words, only parameters “Y” for which allevaluation values of respective elements fall below evaluation values ofother parameters “Y” may be excluded from selection.

Further, in a case where the parameter “Y” includes a plurality ofelements, parameters “Y” which indicate non-inferior solutions to theselection condition for all elements may be selected. Note thatnon-inferior solutions indicate solutions which do not satisfy dominancerelationship even if the solution is compared with any solution in a setof solutions which are available at the present moment. For example, astate where a solution “A” and a solution “B” satisfy relationship of“A_(L)≥B_(L)” in the dimension “L” for all dimensions “L” is referred toas a state where the solution “A” is dominated by the solution “B”, anda solution which is not dominated by any solution is referred to as anon-inferior solution.

Further, in a case where the parameter “Y” includes a plurality ofelements, the parameter “Y” may be selected in descending order of rankof the selection condition. The rank is a set of rank of superiority andinferiority of a set of solutions which approximately follows thefollowing procedure. If a certain set of solutions is provided, a set ofsolutions which become non-inferior solutions from the set of solutionsis set as rank 1, and non-inferior solutions are obtained again from theoriginal set of solutions from which the set of solutions (rank 1) isexcluded, and set as rank 2. Subsequently, non-inferior solutions areobtained again from the original set of solutions from which thenon-inferior solutions (rank 2) are excluded, and set as rank 3. A setof solutions with lower rank may be selected among all the sets ofsolutions subjected to ranking operation until the number of times ofselection reaches a predetermined number.

Further, the evaluation value calculator 112 may select the outputparameter “Y” on the basis of an evaluation value for a specific elementamong a plurality of elements of the output parameter “Y”. Further,while pareto efficient global optimization (ParEGO), expectedhypervolume improvement (EHVI), regret, entropy, or the like, is assumedas a method for calculating the evaluation value, the method may bedetermined as appropriate. Further, there may be one or a plurality ofspecific elements to be evaluated. In a case where there are a pluralityof elements to be evaluated, it is only necessary to use a functionwhich receives two or more values as input and outputs one value as anevaluation function, and the evaluation function may be either linearmapping or non-linear mapping. In a case where there is one element tobe evaluated, a value of the element to be evaluated may be used as theevaluation value as is.

For example, a case where ParEGO is used will be described. In a casewhere elements of the output parameter “Y” to be used for calculation ofthe evaluation value are set as y₀ and y₁, and an i-th element of aweight parameter “W” is set as w_(i), an evaluation value g_(Tche)(y₀,y₁, w_(i)) based on ParEGO is expressed as the following expression.

$\begin{matrix}{{g_{Tche}\left( {y_{0},y_{1},w_{i}} \right)} = {{\max\limits_{j = 0}^{1}\left( {w_{j}y_{j}} \right)} + {0.05\left( {{w_{0}y_{0}} + {w_{1}y_{1}}} \right)}}} & \left\lbrack {{Expression}\mspace{14mu} 4} \right\rbrack\end{matrix}$

Note that respective elements of the weight parameter “W” are sampled soas to satisfy the following expression.

$\begin{matrix}{W = \left\{ {{\left. \left( {w_{0},w_{1}} \right) \middle| {w_{0} + w_{1}} \right. = {1 ⩓ \forall_{j}}},{w_{j} = \frac{l}{s}},{l \in \left( {0,\ldots\;,s} \right)}} \right\}} & \left\lbrack {{Expression}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Note that in a case where the evaluation function is a weighted sum, anarbitrary real number which sets weight of each term of the evaluationfunction may be input. In this event, if coefficients regarding twoelements y₀ and y₁ are set as α and β, the evaluation functiong_(weighted sum) is expressed with the following expression.

g _(weighted sum)(y ₀ ,y ₁)=α·y ₀ +β·y ₁  [Expression 6]

In this manner, by setting elements of the output parameter to beevaluated and the evaluation function in accordance with a series ofprocessing, accuracy of search of parameters is expected to be improved.

The number of the next input parameters may be prevented fromexplosively increasing by designating elements relating to selection,the number of selected elements, or the like, in this manner.

Note that in a case where there is no parameter which satisfies theselection condition, the input parameters do not have to be selected. Inthis case, the parameter generator 111 may adjust the input parameterscreated previously using evolution strategy, random numbers, or thelike.

Note that as described above, there can be a case where a plurality offirst input parameters are provided to the first sub-processing at onetime. In this case, a favorable combination of input parameters isdetermined for each first input parameter. FIG. 9 is a diagramexplaining selection. In the example in FIG. 9, the first inputparameters “X1”, “X2” and “X3” are provided to the first processingapparatus 12A at one time, and 100 second output parameters “Y” aregenerated for each first input parameter to calculate evaluation values“OBJ1” to “OBJ300”. In this case, instead of comparing the evaluationvalues “OBJ1” to “OBJ300”, the evaluation values “OBJ1” to “OBJ100”corresponding to the first input parameter “X1” are compared to performselection, the evaluation values “OBJ101” to “OB3200” corresponding tothe first input parameter “X2” are compared to perform selection, andthe evaluation values “OB3201” to “OBJ300” corresponding to the firstinput parameter “X3” are compared to perform selection. The example inFIG. 9 indicates that a combination of parameters for which “YES” isdescribed on the leftmost column in a table illustrated in FIG. 9 isselected as a favorable combination. In other words, in the example inFIG. 9, (X1, Y2), (X2, Y101) and (X3, Y300) are selected. In thismanner, in a case where a plurality of first input parameters aregenerated, a combination of input parameters is selected for each firstinput parameter.

The storage 114 stores data necessary for processing of the presentembodiment, a processing result, or the like. The data to be stored isnot particularly limited, and, for example, input parameters to be usedfor respective sub-processing, output parameters of the respectivesub-processing, or the like, are stored. Further, in a case whereinformation regarding the respective sub-processing such as executionperiods of the respective sub-processing is received from the respectiveprocessing apparatuses 12, these information may be stored.

FIG. 10 is a diagram illustrating an example of data regarding theselected parameters stored in the storage 114. The example in FIG. 10indicates values of respective elements of the parameters “X” and “Z”selected as satisfying the selection condition. Further, the data alsoincludes values of respective elements of the parameters “U” and “Y”corresponding to the selected parameters “X” and “Z”, evaluation values,and information regarding respective sub-processing. In a case where theinformation regarding the respective sub-processing can be received fromthe respective processing apparatuses 12, the storage 114 may includesuch information in the data.

Further, the storage 114 may also store data including output parametersof the respective processing apparatuses 12 as well as the selectedparameters. The data may also include information regarding therespective sub-processing received from the respective processingapparatuses 12, for example, required periods, or the like.

The determiner 115 determines whether to iterate a series of processingon the basis of a predetermined iteration condition. For example, aseries of processing may be iterated until the number of times ofexecution of a series of processing exceeds a threshold or until arequired period of a series of processing exceeds a threshold.Alternatively, in a case where evaluation is higher as the evaluationvalue is greater, in a case where the evaluation value of the selectedparameter “Y” exceeds a threshold of the iteration condition, iterationmay be stopped. Further, in a case where fluctuation of the evaluationvalue of the selected parameter “Y” successively falls within apredetermined range a predetermined number of times, iteration may bestopped. Still further, a plurality of iteration conditions may beprovided, and in a case where equal to or larger than a predeterminednumber of iteration conditions among the plurality of iterationconditions are satisfied, iteration may be executed or stopped.

In a case where it is determined to iterate a series of processing, thedeterminer 115 instructs the parameter generator 111 to regenerate theinput parameters and as described above, the parameter generator 111generates the input parameters again on the basis of the selected inputparameters. Thus, it can be said that the determiner 115 determineswhether or not to execute regeneration of each input parameter on thebasis of a predetermined condition.

In a case where it is determined not to iterate a series of processing,that is, in a case where it is determined not to regenerate inputparameters, the determiner 115 may determine recommended inputparameters. The recommended input parameters are recommendation valuesof the input parameters. The recommendation values may be valuesdetermined as optimal values by the determiner 115. A value of an inputparameter corresponding to the best evaluation value may be regarded asan optimum value, or the optimum value may be determined by adjusting avalue of the input parameter corresponding to the best evaluation value.For example, in a case where iteration is stopped because a certainevaluation value exceeds a threshold, an input parameter correspondingto the evaluation value may be regarded as the optimum value. Further,in a case where iteration is stopped because a required period of aseries of processing exceeds a threshold, an input parameter withhighest evaluation among the evaluation values so far may be regarded asthe optimum value. Alternatively, an input parameter selected at lastmay be simply regarded as the optimum value.

The input/output device 116 controls input/output to/from the managementapparatus 13. For example, the input/output device 116 may accept inputof predetermined matters to be used for respective processing of theparameter provision apparatus 11, for example, the iteration condition,the selection condition, the parameters of the evaluation function, theelements to be evaluated, or the like, described above. In a case wherethe input is accepted, by the storage 114 updating the storedinformation, processing reflecting the input can be executed. Further,for example, the input/output device 116 may output respectiveprocessing results of the parameter provision apparatus 11 stored in thestorage 114. For example, the input/output device 116 may output therecommended input parameters, a current number of times of iteration,the number of input parameters corresponding to one time of a series ofprocessing, the numbers of times of execution of respectivesub-processing and a series of processing, or the like. Further, theinput/output device 116 may accept correction with respect to outputsettings, or the like. The correction is also reflected by the storage114 updating the information. Further, the input/output device 116 mayaccept answer as to whether the selected input parameters are correct.In this case, values included in the evaluation function, such as aconstant, may be changed so that selected input parameters can approachthe accepted answer.

Further, input and output are performed in a format which is notparticularly limited. For example, the management apparatus 13 may be adisplay apparatus such as a display, and an image indicating a graph, orthe like, of the processing result may be output. A graph only requiresto be generated using a method in related art.

FIG. 11 is a diagram illustrating an example of output. The graphillustrated in FIG. 11 indicates relationship between the number oftimes of execution of a series of processing and an evaluation value ofthe parameter “Y” set as an optimum value. A dotted graph is a graph ina case where the input parameters are randomly generated, and a solidgraph is a graph in a case where the input parameters are generatedusing Bayesian optimization. In this manner, results may be output foreach generation method of the input parameters. This enables the user torecognize a favorable method for generating parameters.

Note that while it has been described that the parameter generator 111transmits parameters to the respective processing apparatuses 12 forexplanatory convenience, the parameters may be input/output to/from therespective processing apparatuses 12 via the input/output device 116.

Flow of processing of the information processing system will bedescribed. FIG. 12 is a schematic flowchart of a series of processing ofthe information processing system.

The parameter generator 111 of the parameter provision apparatus 11generates the input parameters “X” and “Z” (S101). The input parameters“X” and “Z” are generated on the basis of the input parameters “X” and“Z” which are selected previously. In a case where there is no selectedparameter, for example, in a case where the input parameters “X” and “Z”are generated for the first time, the input parameters “X” and “Z” aregenerated on the basis of random numbers, or the like, as describedabove. The first processing apparatus 12A receives the first inputparameter “X” from the parameter provision apparatus 11, executes thefirst sub-processing on the basis of the first input parameter “X” andoutputs the first output parameter “U” (S102). Then, the secondprocessing apparatus 12B executes the second sub-processing on the basisof the second input parameter “Z” and the first output parameter “U” andoutputs the second output parameter “Y” (S103). Both the second inputparameter “Z” and the first output parameter “U” may be acquired fromthe parameter provision apparatus 11 or the first output parameter “U”may be acquired from the first processing apparatus 12A.

The evaluation value calculator 112 of the parameter provision apparatus11 calculates the evaluation value for the second output parameter “Y”(S104), and the selector 113 selects favorable input parameters “X” and“Z” from the input parameters “X” and “Z” generated previously on thebasis of the evaluation value (S105).

The determiner 115 determines whether or not to iterate generation ofthe input parameters “X” and “Z” on the basis of a predeterminedcondition (S106). In a case where it is determined to iterate generation(S107: Yes), the processing returns to the processing in S101, and theparameter generator 111 generates new input parameters “X” and “Z” again(S101). The processing from S101 to S106 is iterated in this manneruntil it is determined not to iterate generation, so that the generatedinput parameters “X” and “Z” become close to appropriate values. In acase where it is determined not to iterate generation (S107: No), thedeterminer 115 determines input parameters “X” and “Z” which aredetermined as optimum values (S108), and the flow ends.

Note that the flowchart in the present description is an example, andorder of the flow may be replaced if the processing can be executed, andrespective processing may be executed in parallel. For example, thedeterminer 115 may determine whether to iterate generation (S106) beforecalculating the evaluation value (S104). Further, the processing in S108can be skipped in a case where it is not necessary to determine theinput parameters “X” and “Z” which are determined as optimum values. Inthis manner, the flow may be adjusted as appropriate.

As described above, according to the present embodiment, in a case wherevalues of parameters of a plurality of sub-processing included in aseries of processing are determined, more candidates for values ofparameters of sub-processing which is to be executed in a shorter periodare provided than candidates for values of parameters of sub-processingwhich is to be executed in a longer period. Further, sub-processingwhich is to be executed in a shorter period is executed a plurality oftimes in one time of a series of processing to reduce the number oftimes of execution of a series of processing. This can preventdegradation of accuracy of values of parameters determined at last whilereducing the number of times of execution of a series of processing.

Note that at least part of the above-described embodiment may beimplemented with a dedicated electronic circuit (that is, hardware) suchas an integrated circuit (IC) in which a processor, a memory, and thelike, are mounted. Further, at least part of the above-describedembodiment may be implemented by executing software (programs). Forexample, it is possible to implement processing of the above-describedembodiment by using a general-purpose computer apparatus as basichardware and causing a processor such as a CPU mounted on the computerapparatus to execute the programs.

For example, the apparatus of the above-described embodiment can beimplemented as a computer by the computer reading out dedicated softwarestored in a computer-readable storage medium. A type of the storagemedium is not particularly limited. Further, the apparatus of theabove-described embodiment can be implemented as a computer by thecomputer installing dedicated software downloaded via a communicationnetwork. In this manner, information processing using software isspecifically implemented using hardware resources.

FIG. 13 is a block diagram illustrating an example of a hardwareconfiguration in one embodiment of the present invention. The parameterprovision apparatus 11 includes a processor 21, a main storage apparatus22, an auxiliary storage apparatus 23, a network interface 24 and adevice interface 25, and can be implemented as a computer apparatus 2 inwhich these are connected via a bus 26. The storage 114 can beimplemented by the main storage apparatus 22 or the auxiliary storageapparatus 23, and components other than the storage 114, such as asoftware execution detector can be implemented by the processor 21.

Note that while the computer apparatus 2 in FIG. 13 includes componentsone each, the computer apparatus 2 may include a plurality of the samecomponents. Further, while FIG. 13 illustrates one computer apparatus 2,software may be installed on a plurality of computer apparatuses, andeach of the plurality of computer apparatuses may execute different partof processing of the software.

The processor 21 is an electronic circuit including a control apparatusand a computation apparatus of the computer. The processor 21 performscomputation processing on the basis of data and a program input fromeach apparatus, or the like, of an internal configuration of thecomputer apparatus 2 and outputs a computation result and a controlsignal to each apparatus, or the like. Specifically, the processor 21controls respective components constituting the computer apparatus 2 byexecuting an operating system (OS) of the computer apparatus 2,application, or the like. The processor 21 is not particularly limited,if the processor 21 can perform the above-described processing.

The main storage apparatus 22 is a storage apparatus which stores acommand, various kinds of data, or the like, to be executed by theprocessor 21, and information stored in the main storage apparatus 22 isdirectly read out by the processor 21. The auxiliary storage apparatus23 is a storage apparatus other than the main storage apparatus 22. Notethat these storage apparatuses mean arbitrary electronic parts in whichelectronic information can be stored, and may be a memory or a storage.Further, while the memory includes a volatile memory and a non-volatilememory, either one may be used.

The network interface 24 is an interface for connecting to thecommunication network 3 in a wireless or wired manner. As the networkinterface 24, it is only necessary to use one complying with existingcommunication standards. It is possible to communicably connect thecomputer apparatus 2 to an external apparatus 4A via the communicationnetwork 3 using the network interface 24 for exchange of information.

The device interface 25 is an interface such as a USB which directlyconnects to an external apparatus 4B. The external apparatus 4B may beeither an external storage medium or a storage apparatus such as adatabase.

The external apparatuses 4A and 4B may be output apparatuses. The outputapparatus may be, for example, a display apparatus which displays animage or an apparatus which outputs speech, or the like. For example,the output apparatus includes, but not limited to, a liquid crystaldisplay (LCD), a cathode ray tube (CRT), a plasma display panel (PDP),and a speaker.

Note that the external apparatuses 4A and 4B may be input apparatuses.The input apparatus includes a device such as a keyboard, a mouse, atouch panel, and provides information input through these devices to thecomputer apparatus 2. A signal from the input apparatus is output to theprocessor 21.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

The description of the application and the claims should be interpretedas generally understood by those skilled in the art and should not beoverly limiting. For example, the statement “using at least one of A andB” means that only A may be used, only B may be used, or both A and Bmay be used.

1. An information processing apparatus comprising: an evaluation valuecalculator configured to calculate an evaluation value for an outputparameter of a series of processing including first processing andsecond processing, the first processing using a first input parameterand the second processing using a second input parameter; a selectorconfigured to select at least part of a plurality of the outputparameters on a basis of the evaluation values; and a parametergenerator configured to regenerate the first input parameter and thesecond input parameter on a basis of a first input parameter and asecond input parameter corresponding to the selected output parameters,wherein the parameter generator generates the first input parameter andthe second input parameter corresponding to one time of the series ofprocessing, and a number of input parameters generated for one of thefirst processing and the second processing which has a shorterprocessing period is larger than a number of input parameters generatedfor the other.
 2. The information processing apparatus according toclaim 1, wherein the parameter generator determines a number of thesecond input parameters to be regenerated, on a basis of an executionperiod per one time of the first processing and an execution period perone time of the second processing.
 3. The information processingapparatus according to claim 2, wherein a ratio of the execution periodper one time of the first processing to the execution period per onetime of the second processing is set as the number of the second inputparameters to be regenerated.
 4. The information processing apparatusaccording to claim 1, wherein the second processing further uses anoutput parameter of the first processing, and in a case where aplurality of the first input parameters corresponding to one time of theseries of processing are generated, the parameter generator generatesthe second input parameter corresponding to each of the first inputparameters.
 5. The information processing apparatus according to claim1, further comprising a determiner configured to determine whether ornot to execute regeneration of the first input parameter and the secondinput parameter on a basis of a predetermined condition and, in a casewhere it is determined not to execute the regeneration, determine arecommendation value of at least one of the first input parameter or thesecond input parameter.
 6. The information processing apparatusaccording to claim 1, further comprising an output device configured tooutput a recommendation value of at least one of the first inputparameter or the second input parameter.
 7. The information processingapparatus according to claim 1, wherein the parameter generatorregenerates the first input parameter and the second input parameter sothat a value of the first input parameter or the second input parameterfalls within a predetermined range.
 8. The information processingapparatus according to claim 1, wherein the evaluation value calculatorcalculates the evaluation values for two or more elements included inthe output parameter of the series of processing, and the selectordetermines an output parameter to be selected on a basis of theevaluation value corresponding to a predetermined element of theelements.
 9. The information processing apparatus according to claim 1,wherein the evaluation value calculator calculates the evaluation valueon a basis of a predetermined evaluation function.
 10. The informationprocessing apparatus according to claim 1, wherein the parametergenerator updates a parameter of a relational expression expressingrelationship between the first input parameter and the evaluation valueon a basis of the first input parameter and the evaluation value, andregenerates the first input parameter on a basis of the relationalexpression including the updated parameter.
 11. The informationprocessing apparatus according to claim 1, further comprising an inputdevice configured to accept information regarding change of apredetermined matter to be used in processing of the evaluation valuecalculator, the selector or the parameter generator, wherein theprocessing of the evaluation value calculator, the selector or theparameter generator is executed using a predetermined matter changed ona basis of information accepted via the input device.
 12. Theinformation processing apparatus according to claim 1, wherein thesecond input parameter is randomly generated.
 13. An informationprocessing method comprising: calculating an evaluation value for anoutput parameter of a series of processing including first processingand second processing, the first processing using a first inputparameter and the second processing using a second input parameter;selecting at least part of a plurality of the output parameters on abasis of the evaluation values; and regenerating the first inputparameter and the second input parameter on a basis of a first inputparameter and a second input parameter corresponding to the selectedoutput parameters, wherein in the regenerating the first input parameterand the second input parameter, the first input parameter and the secondinput parameter corresponding to one time of the series of processingare generated, and a number of input parameters generated for one of thefirst processing and the second processing which has a shorterprocessing period is larger than a number of input parameters generatedfor the other.
 14. A non-transitory storage medium in which a program tobe executed by a computer is stored, the program causing the computer toexecute: calculating an evaluation value for an output parameter of aseries of processing including first processing and second processing,the first processing using a first input parameter and the secondprocessing using a second input parameter; selecting at least part of aplurality of the output parameters on a basis of the evaluation values;and regenerating the first input parameter and the second inputparameter on a basis of a first input parameter and a second inputparameter corresponding to the selected output parameters, wherein inthe regenerating the first input parameter and the second inputparameter, the first input parameter and the second input parametercorresponding to one time of the series of processing are generated, anda number of input parameters generated for one of the first processingand the second processing which has a shorter processing period islarger than a number of input parameters generated for the other.
 15. Aninformation processing system comprising: one or more processingexecution apparatuses configured to execute respective processingincluded in a series of processing including first processing and secondprocessing, the first processing using a first input parameter and thesecond processing using a second input parameter; and an informationprocessing apparatus configured to generate the first input parameterand the second input parameter, wherein the information processingapparatus includes an evaluation value calculator configured tocalculate an evaluation value for an output parameter of the series ofprocessing, a selector configured to select at least part of a pluralityof the output parameters on a basis of the evaluation values, and aparameter generator configured to regenerate the first input parameterand the second input parameter on a basis of a first input parameter anda second input parameter corresponding to the selected outputparameters, the parameter generator generates the first input parameterand the second input parameter corresponding to one time of the seriesof processing, a number of input parameters generated for one of thefirst processing and the second processing which has a shorterprocessing period is larger than a number of input parameters generatedfor the other, the one or more processing execution apparatuses executethe first processing for each of the first input parameter correspondingto one time of the series of processing and execute the secondprocessing for each of the second input parameter corresponding to onetime of the series of processing while the series of processing isexecuted once, and the output parameter is generated for each of thesecond input parameter corresponding to one time of the series ofprocessing.